In this work, we present the Meteorological Neural Network 2 (MetNet-2), a probabilistic weather model based on deep neural networks that is a successor to MetNet [1]}. MetNet-2 features a forecasting range of up to 12 hours of lead time at a frequency of 2 minutes. In order to capture sufficient input context, MetNet-2 uses input observations from a 2048 km \(\times \) 2048 km region and adopts novel neural network architectural elements in order to effectively process the large context. Such elements are (a) a context-aggregating module that enables the receptive field of the network to double after every layer, (b) a strong lead time conditioning scheme and (c) a model parallel training setup utilizing multiple chips for increased memory and parallel computation.
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